4 ms·

Static+strong typing is a nice combination, which is probably what hacker_9 meant to say. ML family is particularly nice -- with Hindley-Milner type inference,

by milcron 10y ago

Static+strong typing is a nice combination, which is probably what hacker_9 meant to say. ML family is particularly nice -- with Hindley-Milner type inference, you get a lot of static guarantees with none of the verbosity of languages like Java.

catnaroek 10y ago
It's not just type inference that makes programming in ML so nice. Also important are: (0) Parametricity: Type variables are never case-analyzed. As a result, types convey useful information about what functions may or may not do. Haskellers call this “free theorems”. Java's `instanceof`, C++'s `sizeof` and template specialization, and GHC's `TypeFamilies` are blatant violations of parametricity that make types less informative. (1) A clear distinction between data (sums of products) and operations (functions). In a general-purpose language, operations will always be somewhat ill-behaved: they may raise exceptions, fail to terminate, etc. Data tends to be better behaved, and algebraic laws can be stated about it, which hold even in the presence of effectful and/or non-terminating functions. This makes ML superior to Haskell, not to mention object-oriented languages. (2) A module system that enforces abstraction boundaries between subsystems of a larger system. ML's opaque signature ascription (which has no counterpart in Haskell or object-oriented languages) ensures that the internal representation of abstract types is only visible in the module where they're implemented. In a well architected ML program, trying to violate another module's internal invariants is a type error!
devishard 10y ago
> Static+strong typing is a nice combination, which is probably what hacker_9 meant to say. That's a rather generous interpretation, given that hacker_9 said nothing about strong types, and instead made a claim about static vs. dynamic languages. I do agree with your assertion, though; learning Haskell and OCaml prepared me for writing Python in ways that many of my peers in the Python community are unprepared. One big example is the difference between str and bytes in Python 3: a lot of Pythonistas find this distinction an annoyance, but I find it extremely useful, largely because I learned how to leverage such type differences to reduce errors from ML family languages.

by milcron 10y ago

catnaroek 10y ago

It's not just type inference that makes programming in ML so nice. Also important are: (0) Parametricity: Type variables are never case-analyzed. As a result, types convey useful information about what functions may or may not do. Haskellers call this “free theorems”. Java's `instanceof`, C++'s `sizeof` and template specialization, and GHC's `TypeFamilies` are blatant violations of parametricity that make types less informative. (1) A clear distinction between data (sums of products) and operations (functions). In a general-purpose language, operations will always be somewhat ill-behaved: they may raise exceptions, fail to terminate, etc. Data tends to be better behaved, and algebraic laws can be stated about it, which hold even in the presence of effectful and/or non-terminating functions. This makes ML superior to Haskell, not to mention object-oriented languages. (2) A module system that enforces abstraction boundaries between subsystems of a larger system. ML's opaque signature ascription (which has no counterpart in Haskell or object-oriented languages) ensures that the internal representation of abstract types is only visible in the module where they're implemented. In a well architected ML program, trying to violate another module's internal invariants is a type error!

devishard 10y ago

> Static+strong typing is a nice combination, which is probably what hacker_9 meant to say. That's a rather generous interpretation, given that hacker_9 said nothing about strong types, and instead made a claim about static vs. dynamic languages. I do agree with your assertion, though; learning Haskell and OCaml prepared me for writing Python in ways that many of my peers in the Python community are unprepared. One big example is the difference between str and bytes in Python 3: a lot of Pythonistas find this distinction an annoyance, but I find it extremely useful, largely because I learned how to leverage such type differences to reduce errors from ML family languages.